12 datasets found

The dataset used in this paper is the MovieLens 1M dataset, which contains a 1M 1-5 star ratings by 6,040 users for 3,952 movies.

The dataset is used for sequential recommendation tasks, and it contains user-item interaction history.

Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions.

The associated task is to predict the movie rating on a 5-star scale. This dataset contains 6,040 users, 3,900 movies, and 1,000,209 ratings, i.e., rating matrix is 4.26% full.

The dataset used in the paper is MovieLens Latest, MovieLens 1m, MovieLens 10m, and Yelp.

The dataset used in this paper is a high-rating movie recommendation system. The objective of the system is to recommend high-rating movies to users, but the ratings for the...

The MovieLens Boxoffice dataset is a large-scale movie recommendation dataset. It contains 99326 user-item interaction records, with each record representing a user's rating of...

The MovieLens 20m Light dataset is a large-scale movie recommendation dataset. It contains 20 million user-item interaction records, with each record representing a user's...

The MovieLens dataset contains questions answerable using Wikidata as the knowledge graph, focusing on questions with a single entity and relation.

The dataset is a movie review dataset with five types of nodes (movie, director, tag, writer, and user) and four types of edges (movie-director relation, movie-tag relation,...

A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K...

The dataset used in this paper is the MovieLens 20M dataset, which contains ratings from 92,032 users on 20,000 movies.